Model identification for time series with dependent innovations
作者： Shuhao Chen , Wanli Min , Rong Chen
DOI: 10.5705/SS.2010.219
关键词:
摘要: This paper investigates the impact of dependent but uncorrelated inno- vations (errors) on traditional autoregressive moving average model (ARMA) order determination schemes such as autocorrelation function (ACF), partial auto- correlation (PACF), extended (EACF), and unit-root test. The ARMA models with iid innovations have been studied exten- sively are well-posed, their properties relatively less studied. In presence innovations, we show that ACF, PACF, EACF significantly impacted while unit- root test is not affected. We also propose a new scheme to address those impacts for analyzing time series innovations.
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